Open AccessAutomatic Generation of Urban Road Planning Network Under Deep Learning
Author(s) -
Qikang Zhong,
Qiuhong Liu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2074/1/012088
Subject(s) - urbanization , computer science , domain (mathematical analysis) , transport engineering , urban planning , deep learning , traffic congestion , sustainable development , artificial neural network , noise pollution , artificial intelligence , civil engineering , engineering , noise reduction , mathematical analysis , mathematics , law , political science , economics , economic growth
With the rapid advancement of China’s urbanization process and the rapid increase of the number of motor vehicles, now the vast majority of cities in China are faced with traffic congestion, environmental pollution, noise pollution and other problems. Facing these problems, a road network with reasonable structure, proper layout and sufficient capacity has become an important basic condition for the sustainable development of urban traffic system. This paper mainly studies the automatic generation method of urban road planning network based on deep learning. In this paper, a model based on deep neural network is proposed, which integrates the knowledge of road planning domain and generative adversarial network, and can realize the generation of road network simply and quickly.